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Advances in Computer Simulation of Genome Evolution: Toward More Realistic Evolutionary Genomics Analysis by Approximate Bayesian Computation

NGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implic... Full description

Journal Title: Journal of molecular evolution 2015-03-26, Vol.80 (3-4), p.189-192
Main Author: Arenas, Miguel
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: New York: Springer US
ID: ISSN: 0022-2844
Link: https://www.ncbi.nlm.nih.gov/pubmed/25808249
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title: Advances in Computer Simulation of Genome Evolution: Toward More Realistic Evolutionary Genomics Analysis by Approximate Bayesian Computation
format: Article
creator:
  • Arenas, Miguel
subjects:
  • Analysis
  • Animal Genetics and Genomics
  • Approximate Bayesian computation
  • Bayes Theorem
  • Biomedical and Life Sciences
  • Cell Biology
  • Computer Simulation
  • Computer simulations
  • Computer-generated environments
  • Evolution
  • Evolution, Molecular
  • Evolutionary Biology
  • Genetic research
  • Genome
  • Genome evolution
  • Genomics
  • High-Throughput Nucleotide Sequencing
  • Letter to the Editor
  • Life Sciences
  • Microbiology
  • Models
  • Models, Genetic
  • Molecular evolution
  • Plant Genetics and Genomics
  • Plant Sciences
  • Population genetics
  • Sequence Analysis, DNA
ispartof: Journal of molecular evolution, 2015-03-26, Vol.80 (3-4), p.189-192
description: NGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implicit complexity, can co-occur and confound ancestral inferences. Recently, models of genome evolution that accommodate such complex genomic events are emerging. This letter explores these novel evolutionary models and proposes their incorporation into robust statistical approaches based on computer simulations, such as approximate Bayesian computation, that may produce a more realistic evolutionary analysis of genomic data. Advantages and pitfalls in using these analytical methods are discussed. Potential applications of these ancestral genomic inferences are also pointed out.
language: eng
source:
identifier: ISSN: 0022-2844
fulltext: no_fulltext
issn:
  • 0022-2844
  • 1432-1432
url: Link


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descriptionNGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implicit complexity, can co-occur and confound ancestral inferences. Recently, models of genome evolution that accommodate such complex genomic events are emerging. This letter explores these novel evolutionary models and proposes their incorporation into robust statistical approaches based on computer simulations, such as approximate Bayesian computation, that may produce a more realistic evolutionary analysis of genomic data. Advantages and pitfalls in using these analytical methods are discussed. Potential applications of these ancestral genomic inferences are also pointed out.
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subjectAnalysis ; Animal Genetics and Genomics ; Approximate Bayesian computation ; Bayes Theorem ; Biomedical and Life Sciences ; Cell Biology ; Computer Simulation ; Computer simulations ; Computer-generated environments ; Evolution ; Evolution, Molecular ; Evolutionary Biology ; Genetic research ; Genome ; Genome evolution ; Genomics ; High-Throughput Nucleotide Sequencing ; Letter to the Editor ; Life Sciences ; Microbiology ; Models ; Models, Genetic ; Molecular evolution ; Plant Genetics and Genomics ; Plant Sciences ; Population genetics ; Sequence Analysis, DNA
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abstractNGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implicit complexity, can co-occur and confound ancestral inferences. Recently, models of genome evolution that accommodate such complex genomic events are emerging. This letter explores these novel evolutionary models and proposes their incorporation into robust statistical approaches based on computer simulations, such as approximate Bayesian computation, that may produce a more realistic evolutionary analysis of genomic data. Advantages and pitfalls in using these analytical methods are discussed. Potential applications of these ancestral genomic inferences are also pointed out.
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